About this Research Topic
In mobile robotics, Simultaneous Localization and Mapping (SLAM) is a highly relevant topic that has been investigated for years because of its importance in several applications, such as exploration of hard-to-reach places, underwater exploration, surveillance with Unmanned Air Vehicles (UAV), and self-driving cars. SLAM enables robots to build maps and track their locations in the environment, thus facilitating autonomy. Recently, Semantic SLAM, which combines SLAM with object identification, has gained attention. Traditional SLAM provides only geometric data, while semantic analysis recognizes objects in the surroundings. The integration of these techniques allows for the creation of maps enriched with semantic information, enhancing navigation and task planning capabilities. For instance, in hospitals, robots equipped with Semantic SLAM can navigate safely in high-risk areas, reducing human exposure to diseases like COVID-19. Despite these advancements, there is still a need for more robust autonomous navigation systems that can operate safely in dynamic, unknown, and mixed indoor/outdoor environments, mimicking human behaviors such as obstacle avoidance and respecting personal space.
This Research Topic aims to explore the development and enhancement of Semantic SLAM for mobile robot navigation. The main objectives include investigating how semantic information can be integrated into SLAM to improve localization, mapping, and navigation tasks. Specific questions to be addressed include: How can object recognition systems be optimized for real-time applications? What are the best practices for integrating machine learning algorithms into SLAM? How can multi-robot systems benefit from Semantic SLAM? The research will also test hypotheses related to the efficiency and accuracy of Semantic SLAM in various environments.
To gather further insights into the boundaries of Semantic SLAM for mobile robot navigation, we welcome articles addressing, but not limited to, the following themes:
- Applications for Mobile Robot Navigation
- Application of Machine Learning in SLAM Algorithms
- Visual SLAM
- Multi-Robot SLAM
- Semantic SLAM
This Research Topic aims to explore the development and enhancement of Semantic SLAM for mobile robot navigation. The main objectives include investigating how semantic information can be integrated into SLAM to improve localization, mapping, and navigation tasks. Specific questions to be addressed include: How can object recognition systems be optimized for real-time applications? What are the best practices for integrating machine learning algorithms into SLAM? How can multi-robot systems benefit from Semantic SLAM? The research will also test hypotheses related to the efficiency and accuracy of Semantic SLAM in various environments.
To gather further insights into the boundaries of Semantic SLAM for mobile robot navigation, we welcome articles addressing, but not limited to, the following themes:
- Applications for Mobile Robot Navigation
- Application of Machine Learning in SLAM Algorithms
- Visual SLAM
- Multi-Robot SLAM
- Semantic SLAM
Keywords: Semantic SLAM, SLAM, Localization, Mapping, Navigation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
